The Autism spectrum disorders (ASD) are devastating neurodevelopmental disorders with a rising prevalence in the United States that is currently estimated at 1 in 110. The cost of ASD to affected people, families, and society is enormous. Research aimed at uncovering the pathogenesis of this condition, and potentially leading to rational approaches to prevention or treatment, is of the greatest importance. We propose here to use multi- modal neuroimaging to identify biomarkers of risk for autism. The discovery of reliable biomarkers will aid in the identification of individuals with ASD as well as those who will subsequently develop or are already developing subtle signs of ASD. In addition, biomarkers could serve to identify early biological risk factors for ASD, ultimately allowing us to achieve the goal of preventing the development of the disorder in people at risk or reducing the degree of severity in those affected. Biomarkers related to increased brain volume derived from structural magnetic resonance imaging (MRI) have revealed differences between individuals with ASD and typically-developing controls. Functional MRI (fMRI) differences have also been found (for instance, using tasks related to face identity and facial expression as shown by our group). Recent evidence suggests altered brain connectivity from both structural (based on diffusion tensor imaging, DTI) and functional measures in ASD. However, all of these alterations are quite subtle and the findings have been inconsistent. It is our hypothesis in our proposed work that the use of anatomic and diffusion information to guide and constrain the extraction of ASD-related subnetworks whose definition is based on both functional signals and connectivity information will provide more sensitive and robust image-derived biomarkers for ASD. Thus, we focus our efforts on the development of a unique mathematical approach that will estimate three functionally-connected subnetworks in the brain related to ASD and a motion perception task. We will use a multi-view integration strategy to jointly consider fMRI time course strength/coherence and DTI-based structural paths. This approach will: i.) estimate voxels where activation is most likely in response to our motion perception task and compute model-enhanced activation regression parameters, ii.) use these voxels to estimate structurally-informed, functionally-connected subnetworks and iii.) derive important ASD-related parameters of activation signal strength and connectivity that can be used as quantitative biomarkers. We will first apply the strategy to typically developing children and confirm the utility of our measures by illustrating that our approach can produce reliable biomarker information in comparison to a large number of fMRI runs and show that this information is reproducible over multiple acquisitions. Then, we will demonstrate the effectiveness of our new biomarkers by examining the signal change and connectivity parameters derived from three ASD-related functional subnetworks that respond to our motion perception task. We will evaluate how well these measures can stratify three subject groups: children with ASD, unaffected siblings of children with ASD and typically developing children. We will then compare the results to three alternative strategies.